Copy-Paste augmentation.
Pasting foreground objects on a background is a widely used technique in many vision applications [1]}, [2]}, [3]}.
Recently, copy-and-paste augmentation was shown to be a very useful technique in instance segmentation [4]}, [5]}, [2]}. Dwibedi  [5]} proposed to synthesize an instance segmentation dataset by pasting object instances on diverse backgrounds and trained on the augmented images in addition to the original dataset. Dvornik  [4]} considered modeling the visual context to paste the objects while Ghiasi  [2]} showed that pasting objects randomly is good enough to provide solid gains. These methods still assume a closed-world setting, whereas our task is the open-world instance segmentation problem.
There are two technical differences compared to these methods.
First, our augmentation samples background images from a small region of an original image to create a background unlikely to have any objects. This pipeline is designed to circumvent suppressing hidden objects as background and does not require any external background data as used in [5]}.
Second, we decouple the training into two parts, which is also key to achieving a well-performing open-world detection model. In contrast, all of the existing approaches above simply train on synthesized images.
